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Rough Neural Networks in Adapting Cellular Automata Rule for Reducing Image Noise

Authors: Yasser F. Hassan

Abstract:

The reduction or removal of noise in a color image is an essential part of image processing, whether the final information is used for human perception or for an automatic inspection and analysis. This paper describes the modeling system based on the rough neural network model to adaptive cellular automata for various image processing tasks and noise remover. In this paper, we consider the problem of object processing in colored image using rough neural networks to help deriving the rules which will be used in cellular automata for noise image. The proposed method is compared with some classical and recent methods. The results demonstrate that the new model is capable of being trained to perform many different tasks, and that the quality of these results is comparable or better than established specialized algorithms.

Keywords: Rough Sets, Rough Neural Networks, Cellular Automata, Image Processing.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1336929

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References:


[1] Kaliraj, G., Baskar, S., An efficient approach for the removal of impulse noise from the corrupted image using neural network based impulse detector, Image and vision computing, 28, pp. 458-466, 2010.
[2] Yasser Hassan, Rough Set Adaptive in the Model Based of Cellular Automata and Multi-Agents, Journal of Emerging Trends in computing and Information Sciences, Vol.2 No.8, 2011.
[3] Yasser Hassan, rough sets for adapting wavelet neural networks as a new classifier system, Springer applied intelligence journal (35), pp. 260-268, 2011.
[4] Li H., Liao X., Li C., Huang H., and Li Ch., Edge detection of noisy images based on cellular neural networks, Common nonlinear sci number simulat 16, pp. 3746-3769, 2011.
[5] Lin T., Decision-based fuzzy image restoration for noise reduction based on evidence theory, Expert system with applications 38, pp. 8303-8310, 2011.
[6] Musgrif M. and Ray A., Color image segmentation: rough-set theoretic approach, pattern recognition letters 29, pp. 483-493, 2008.
[7] Paul L. Rosin, Image processing using 3-state rough set, Computer Vision and Image Understanding 114, 790–802, 2010.
[8] Porter, R., Frigo, J., Conti, A., Harvey, N., Kenyon, G. and Gokhale, M., A reconfigurable computing framework for multi-scale cellular image processing, Microprocessors and Microsystems 31, 546-563, 2007.
[9] Rosin, P., Image processing using 3-state cellular automata, Computer vision and image understanding, 114, pp. 790-802, 2010.
[10] Sinha D. andLaplante P., A rough set-based approach to handling spatial uncertainty in binary images, Engineering applications of artificial intelligence 17, 97-110, 2004.
[11] Terrazas, G., Slepmann, P., Kendall, G., and Krasnogor, N., An evolutionary methodology for automated design of cellular automaton-based complex systems, Journal of rough set, vol. pp. 77-102, 2007.